Where OpenAI may fit
OpenAI capabilities may support assistants, agents, content and knowledge workflows, analysis, customer-service augmentation and decision support. The architecture must define the permitted context, tools and actions; separate model inference from business authority; evaluate quality and failure modes; and provide monitoring, human intervention and recovery paths.
Decisions the architecture must make explicit
- 01
Which use cases justify model-based behaviour, and which decisions require deterministic rules or human approval?
- 02
What data and tools may OpenAI services access, and how will consent, privacy and retention requirements be enforced?
- 03
How will output quality, safety, latency, cost and failure behaviour be tested and monitored?
- 04
Where should agents be allowed to act, and what approval, audit and recovery controls are required?
- 05
What level of model and provider portability is required for the application architecture?
Where independent advice can help
- Frame a bounded OpenAI use-case, provider or build-versus-buy decision.
- Assess customer context, data access, AI and decisioning readiness, governance and operating ownership.
- Define evaluation criteria, deterministic guardrails, human-control points and provider boundaries.
- Review implementation architecture and delivery decisions without representing Enterprise Martech as an OpenAI implementation partner.
Boundaries and non-fit
- Prompt production, model training or production application development
- Security, privacy, legal or AI-safety certification
- Guaranteed model accuracy, performance, cost or availability
- A predetermined recommendation that must select OpenAI